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Signal Processing Machine Learning Jobs in Georgia

Athena Technology Group

Process modeling data and perform advanced analytics * Apply machine learning, natural language processing, and network analysis techniques * Develop innovative approaches to analyze large-scale ...

AEG

Staff Machine Learning Engineer

Atlanta, GA · On-site

$220K - $280K/yr

... application process on the follow-up screen. At PrizePicks, we are the fastest-growing sports ... As a Staff Machine Learning Engineer, you will lead the technical charge to scale and productionize ...

AppFolio

Sr. Machine Learning Engineer

GA · Remote

$100.50K - $138K/yr

Flows: an agentic workflow system enabling automation of complex business processes * Performers ... Who we are looking for We're seeking a Sr Machine Learning Engineer to play a critical role in ...

Workday

Sr Machine Learning Engineer

Atlanta, GA

$159.60K - $276K/yr

Proven understanding of machine learning algorithms (supervised, unsupervised) and model evaluation ... Recruiters can share more detail during the hiring process. Each candidate's compensation offer ...

AppFolio

Sr. Machine Learning Engineer

Atlanta, GA · Remote

$100.50K - $138K/yr

Flows: an agentic workflow system enabling automation of complex business processes * Performers ... Who we are looking for We're seeking a Sr Machine Learning Engineer to play a critical role in ...

PrizePicks

Staff Machine Learning Engineer

Atlanta, GA · On-site +1

$220K - $280K/yr

As a Staff Machine Learning Engineer, you will lead the technical charge to scale and productionize ... the hiring process. This application period will remain open for 30 days. We're committed to ...

Apex Systems

Communications Engineer

Atlanta, GA

$92K - $160K/yr

The role involves working on projects that fuse advanced signal processing, adaptive communications technologies, machine learning, and sophisticated modeling to develop solutions for cognitive ...

Dolby

Sr Data Researcher -Agentic Systems

Atlanta, GA · On-site

... digital signal processing, audio engineering, image processing, computer vision, data science ... machine learning and data mining, data visualization, natural language processing and big data ...

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Signal Processing Machine Learning information

See Georgia salary details

$45.2K

$110.9K

$163.4K

How much do signal processing machine learning jobs pay per year?

As of May 29, 2026, the average yearly pay for signal processing machine learning in Georgia is $110,909.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,600.00 and $124,500.00 per year, depending on experience, location, and employer.

What is a Signal Processing Machine Learning job?

A Signal Processing Machine Learning job involves developing algorithms that analyze and process signals (such as audio, images, video, or sensor data) using machine learning techniques. Professionals in this role apply concepts from digital signal processing (DSP) to extract meaningful patterns, enhance signal quality, and improve data-driven predictions. They work in diverse fields like telecommunications, biomedical engineering, finance, and autonomous systems. Typical tasks include feature extraction, noise reduction, and deploying deep learning models for real-time signal interpretation. Strong skills in mathematics, programming (Python, MATLAB), and frameworks like TensorFlow or PyTorch are essential.

What are the key skills and qualifications needed to thrive in the Signal Processing Machine Learning position, and why are they important?

To thrive in Signal Processing Machine Learning, you need a strong background in mathematics, digital signal processing, and machine learning, generally supported by a relevant degree in electrical engineering, computer science, or a related field. Experience with programming languages such as Python or MATLAB, familiarity with frameworks like TensorFlow or PyTorch, and knowledge of signal processing libraries are typically required. Analytical thinking, problem-solving ability, and effective communication are crucial soft skills in this position. These competencies enable you to design, implement, and refine advanced algorithms that address complex, real-world data challenges.

What are some typical projects or responsibilities for a Signal Processing Machine Learning professional?

As a Signal Processing Machine Learning professional, you can expect to work on projects that involve developing and optimizing algorithms for tasks such as audio or image recognition, anomaly detection, or sensor data analysis. Daily responsibilities often include pre-processing and cleaning large datasets, feature extraction, building and training machine learning models, and validating system performance. Collaboration with cross-functional teams—such as hardware engineers, data scientists, and software developers—is common to integrate your solutions into products or services. The work environment is typically dynamic and may involve both research-oriented tasks and practical implementation to create impactful, data-driven applications.
More about Signal Processing Machine Learning jobs
What are popular job titles related to Signal Processing Machine Learning jobs in Georgia? For Signal Processing Machine Learning jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Signal Processing Machine Learning jobs in Georgia look for? The top searched job categories for Signal Processing Machine Learning jobs in Georgia are:
Signal Processing Machine Learning jobs near you
Infographic showing various Signal Processing Machine Learning job openings in Georgia as of May 2026, with employment types broken down into 96% Full Time, 1% Part Time, 1% Temporary, 1% Contract, and 1% Nights. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $110,909 per year, or $53.3 per hour.

Machine Learning Engineer

Five and Fly, LLC.

Atlanta, GA

Full-time

Posted 5 days ago

Job description

We are seeking a skilled and forward-looking ML Engineer with experience in Large Language Models (LLMs), generative AI, and agentic architectures to join our growing R&D and Applied AI team. This role is critical in helping Oversight deliver the next generation of agentic AI systems for enterprise spend management and risk controls.
 
The ideal candidate has a strong foundation in machine learning, modern deep learning frameworks, and data pipelines, coupled with hands-on experience experimenting with LLMs, small language models (SLMs), multi-agent frameworks, and retrieval-augmented generation (RAG).

You will work closely with AI/ML researchers, data engineers, and product teams to design, implement, and optimize models that power autonomous exception resolution, anomaly detection, and explainable insights. This is a hands-on engineering role where you will not only build and scale ML systems but also actively contribute to cutting-edge applied research in agentic AI.
Core ML/LLM Engineering
  • Contribute to the design, training, fine-tuning, and deployment of ML/LLM models for production.
  • Implement RAG pipelines using vector databases.
  • Work with frameworks like LangChain, LangGraph, MCP to prototype and optimize multi-agent workflows.
  • Develop prompt engineering, optimization, and safety techniques for agentic LLM interactions.
  • Integrate memory, evidence packs, and explainability modules into agentic pipelines.
  • Work hands-on with multiple LLM ecosystems:
    • OpenAI GPT models (GPT-4, GPT-4o, fine-tuned GPTs).
    • Anthropic Claude (Claude 2/3 for reasoning and safety-aligned workflows).
    • Google Gemini (multimodal reasoning, advanced RAG integration).
    • Meta LLaMA (fine-tuned/custom models for domain-specific tasks).
Data & Infrastructure
  • Collaborate with Data Engineering to build and maintain real-time and batch data pipelines that serve ML/LLM workloads.
  • Conduct feature engineering, preprocessing, and embeddings generation for structured and unstructured data.
  • Implement model monitoring, drift detection, and retraining pipelines.
  • Leverage cloud ML platforms (AWS Sagemaker, Databricks ML) for experimentation and scaling.
Research & Applied Innovation
  • Explore and evaluate emerging LLM/SLM architectures and agent orchestration patterns.
  • Experiment with generative AI and multimodal models to extend capabilities beyond text (images, structured financial data).
  • Collaborate with R&D to prototype autonomous resolution agents, anomaly detection models, and reasoning engines.
  • Translate research prototypes into production-ready components.
Collaboration & Delivery
  • Work cross-functionally with R&D, Data Science, Product, and Engineering to deliver business-aligned AI features.
  • Participate in design reviews, architecture discussions, and model evaluations.
  • Document processes, experiments, and results effectively for knowledge sharing.
  • Mentor junior engineers and contribute to ML engineering best practices.
Required
  • Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.
  • 3+ years of experience building and deploying ML systems.
  • Proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.
  • Hands-on experience with LLMs/SLMs (fine-tuning, prompt design, inference optimization).
  • Demonstrated experience with at least two of the following ecosystems:
    1. OpenAI GPT models (chat, assistants, fine-tuning).
    2. Anthropic Claude (safety-first AI for reasoning and summarization).
    3. Google Gemini (multimodal reasoning, enterprise-scale APIs).
    4. Meta LLaMA (open-source, fine-tuned models).
  • Familiarity with vector databases, embeddings, and RAG pipelines.
  • Ability to work with structured and unstructured data at scale.
  • Knowledge of SQL and distributed data frameworks (Spark, Ray).
  • Strong understanding of ML lifecycle: data prep, training, evaluation, deployment, monitoring.
Preferred Qualifications
  • Experience with agentic frameworks (LangChain, LangGraph, MCP, AutoGen).
  • Knowledge of AI safety, guardrails, and explainability techniques.
  • Hands-on experience deploying ML/LLM solutions in cloud environments (AWS, GCP, Azure).
  • Experience with CI/CD for ML (MLOps), monitoring, and observability.
  • Familiarity with anomaly detection, fraud/risk modeling, or behavioral analytics.
  • Contributions to open-source AI/ML projects or publications in applied ML research.
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3D0X4 / 1D7X1 (Software / Data Ops variants)

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CTN - Cryptologic Technician (Networks)
CTI / CTR (with analytics focus)
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1721 - Cyberspace Warfare Operator
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US Space Force:
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